Data Analytics in Business Intelligence: How Much Further Can LLMs Take It?

Large Language Models (LLMs) changed everything about data analytics and business intelligence. LLMs are turning raw data into comprehensible and actionable data insights to grow a business. They can be trained on up to one petabyte of data based on billions or sometimes trillions of parameters. Putting it simply – they’re analyzing data like a human couldn’t.

The question is: how much further can LLMs take it? Read on to find out.

What Are Data Analytics and LLMs?

Data analytics converts information into actionable insights.

Business intelligence tools use data analytics principles to monitor performance, forecast future performance, etc. Typically, these processes depend on structured data forms, including spreadsheets, metrics, and business intelligence databases. Until relatively recently, a person behind a computer would collect the data – now, we have LLMs collecting mass amounts of data we could only dream about.

LLMs are artificial intelligence models trained on large datasets to analyze and comprehend human language and information. One example we’ve all heard about is ChatGPT. Businesses can take models similar to ChatGPT, train them specifically to the business, and have a comprehensive insight into the data that matters the most.

How LLMs Collect Data

It starts with training. Pre-training involves giving models a large corpus of available data, including books, articles, and the web. Most of us don’t realize it, but most of the data LLMs train on comes from the information we should avoid leaving online. Or, going back to the example of ChatGPT, the generic GPT-4 model has been trained using manifold text samples from endless domains. This form of pre-training is why LLMs can process language, identify different patterns, generate meaningful text, or for business analytics, valuable insights.

After pre-training comes business-related datasets. LLMs can analyze information from customer feedback, product reviews, or internal reports. And most of the data it analyzes is unstructured – according to the IBM report, that’s more than 80% of all the data in any organization.

How Much Further Can LLMs Take Data Analytics?

Back to the question in the title – how much further can LLMs take data analytics?

Monolithic LLMs – trained on structured and unstructured data sources – seem to know no bounds. For example, in customer care, ordinary analytics would track the number of complaints and the average time taken to resolve those complaints and that time frame. LLMs can go a step further and look at the in-text of the actual complaints and do sentiment analysis, outlining the emotional trends, pain points, and product concerns that would have been lost in the analysis quantitatively.

LLMs also improve predictive analytics – or forecasting in general – by enabling users to interpret the situation differently from how traditional models operate. Advanced NLP models or ERM analytics can notice tensions within phrases, movement anticipations from dated texts, or even analyze content to obtain general ideas.

It’s predicted that LLMs could contribute over $15.7 trillion to the world economy by the year 2030, according to PwC.

We think LLMs will take it further by giving deeper, more accurate insights to grow businesses.

How Is It Benefiting Business Intelligence?

How isn’t it benefiting business intelligence? That should be the question.

LLMs minimize the time needed to process big datasets, enhancing decision-making within organizations. According to Gartner, by 2025, AI and machine learning technologies will dominate the business intelligence industry.

LLMs also offer improved personalization. Companies can use customer information more efficiently and present customized goods and promotions. According to McKinsey, there’s a predicted revenue increase of 5-15% and a maximum predicted cost reduction of 30% for companies focusing on personalization.

Every business should be using LLMs for data analytics in business intelligence – there’s simply no other way to do it effectively. They’re data-led experts in data analytics with more processing power than any human. It’ll be interesting to see how the technology develops in the next ten years to go even deeper into data analytics.